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Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength

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  • Muhammad Saqib Jan

    (School of Art, Anhui University of Finance & Economics, Bengbu 233030, China
    Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
    These authors contributed equally to this work.)

  • Sajjad Hussain

    (School of Art, Anhui University of Finance & Economics, Bengbu 233030, China
    Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
    These authors contributed equally to this work.)

  • Rida e Zahra

    (Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Muhammad Zaka Emad

    (Department of Mining Engineering, University of Engineering and Technology, Lahore 39161, Pakistan)

  • Naseer Muhammad Khan

    (Department of Sustainable Advanced Geomechanical Engineering, Military College of Engineering, National University of Sciences and Technology, Risalpur 23200, Pakistan)

  • Zahid Ur Rehman

    (Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Kewang Cao

    (School of Art, Anhui University of Finance & Economics, Bengbu 233030, China
    School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea)

  • Saad S. Alarifi

    (Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Salim Raza

    (Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Saira Sherin

    (Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Muhammad Salman

    (Department of Civil Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

Abstract

Rock strength, specifically the uniaxial compressive strength (UCS), is a critical parameter mostly used in the effective and sustainable design of tunnels and other engineering structures. This parameter is determined using direct and indirect methods. The direct methods involve acquiring an NX core sample and using sophisticated laboratory procedures to determine UCS. However, the direct methods are time-consuming, expensive, and can yield uncertain results due to the presence of any flaws or discontinuities in the core sample. Therefore, most researchers prefer indirect methods for predicting rock strength. In this study, UCS was predicted using seven different artificial intelligence techniques: Artificial Neural Networks (ANNs), XG Boost Algorithm, Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), Lasso, and Ridge models. The input variables used for rock strength prediction were moisture content (MC), P-waves, and rebound number (R). Four performance indicators were used to assess the efficacy of the models: coefficient of determination (R 2 ), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). The results show that the ANN model had the best performance indicators, with values of 0.9995, 0.2634, 0.0694, and 0.1642 for R 2 , RMSE, MSE, and MAE, respectively. However, the XG Boost algorithm model performance was also excellent and comparable to the ANN model. Therefore, these two models were proposed for predicting UCS effectively. The outcomes of this research provide a theoretical foundation for field professionals in predicting the strength parameters of rock for the effective and sustainable design of engineering structures

Suggested Citation

  • Muhammad Saqib Jan & Sajjad Hussain & Rida e Zahra & Muhammad Zaka Emad & Naseer Muhammad Khan & Zahid Ur Rehman & Kewang Cao & Saad S. Alarifi & Salim Raza & Saira Sherin & Muhammad Salman, 2023. "Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength," Sustainability, MDPI, vol. 15(11), pages 1-24, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8835-:d:1159815
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    References listed on IDEAS

    as
    1. Sajjad Hussain & Naseer Muhammad Khan & Muhammad Zaka Emad & Abdul Muntaqim Naji & Kewang Cao & Qiangqiang Gao & Zahid Ur Rehman & Salim Raza & Ruoyu Cui & Muhammad Salman & Saad S. Alarifi, 2022. "An Appropriate Model for the Prediction of Rock Mass Deformation Modulus among Various Artificial Intelligence Models," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    2. Niaz Muhammad Shahani & Xigui Zheng & Xiaowei Guo & Xin Wei, 2022. "Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
    3. Naseer Muhammad Khan & Kewang Cao & Muhammad Zaka Emad & Sajjad Hussain & Hafeezur Rehman & Kausar Sultan Shah & Faheem Ur Rehman & Aamir Muhammad, 2022. "Development of Predictive Models for Determination of the Extent of Damage in Granite Caused by Thermal Treatment and Cooling Conditions Using Artificial Intelligence," Mathematics, MDPI, vol. 10(16), pages 1-22, August.
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    Cited by:

    1. Kadir Karaman & Hasan Kolaylı, 2024. "Effects of Olivine Alteration on Micro-Internal Structure and Geomechanical Properties of Basalts and Strength Prediction in These Rocks," Sustainability, MDPI, vol. 16(13), pages 1-17, June.

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